Hi! I’m a data scientist in industry, with a background in social science research. I am deeply interested in programming, sociology, the data science industry (and making it better), and education. If you’re in need of a speaker on one of these topics, let me know!
Also, I am committed to helping other women get into the data science field, so if you need advice or support, contact me!
I code in R, SQL, and Python, using RStudio and SublimeText/Atom as my preferred IDE/code editors, or Jupyter Labs for some Python projects. (I’m also learning Scala!)
To see more about what kinds of data science I do, check out my projects on this page, or my Github profile.
To manage code and tasks, I use Git and Airflow. I also have experience with Elasticsearch databases and R and Python package development.
This site is built in RMarkdown and a bit of Javascript.
I enjoy doing data projects in my spare time, and while you can find most of them on kaggle or github, here are direct links to some of my favorites.
I recently co-taught a daylong course for a group of 30 women/gender nonbinary students about how to write R packages- we had a really good time! I analyzed our pre- and post- surveys in a notebook, to check how effective the day was for students.
This project is a kaggle kernel, in which I walked the reader through the process of cleaning and modeling the data from a real estate prices dataset, using linear modeling, random forests, and gradient boosting (xgboost). My most popular kernel to date! This one also produced respectable competition results, and was chosen for special recognition by the Kaggle admins. (I won a mug!)
Update: Read the interview I did regarding this project (and the other fabulous winners)! http://blog.kaggle.com/2017/03/29/predicting-house-prices-playground-competition-winning-kernels
Key Skills: machine learning, data cleaning
I led a team working on the Chicago Lobbying project, which produced some great output, including this visualization of lobbying and aldermen in Chicago. The project is continuing and building out new functionality. I personally cleaned some of the data underlying, but my biggest contribution was organizing, planning, and leadership. Additional results: https://data.world/lilianhj/chicago-lobbyists
Update: Check out a case study by the fine folks at data.world discussing the work that went in to this project: https://medium.com/@sharonbrener/dbf30aeee70b
Among the public datasets available on Kaggle is this one, describing the crimes that have occurred in Austin, TX over a couple of years. This project cleans the data, does some exploratory analysis, and maps various kinds of crime by district
Key Skills: data cleaning, GIS
stephanie@stephaniekirmer.com
Kaggle | Twitter | Github | Linkedin
See what I’m reading on Pocket: http://getpocket.com/@data_stephanie
You can see my talk about integrating Elasticsearch storage with R or Python analytics workflows on May 24 in NYC at Rev!
If you missed my keynote at the very first annual SatRdays Chicago Event on April 27, you can watch the whole talk here, alongside all the other excellent talks! To see my slides close up, head over to my github.
If you came to see me speak about R packages for team collaboration, you can get the slides and supporting materials on Github. If you have questions or need help producing your own packages, hit me up on twitter.